Sawmill Production Planning
If commerce sets the headroom, planning decides how much of it you actually capture. Planning is the highest-leverage lever in most sawmills and the one most frequently underbuilt, because it tends to live in spreadsheets and one technologist's head. This article explains why short-horizon planning loses, what changes when you plan on a window, and what good sawmill optimization looks like in practice — built around modern sawing batch planning and a real yield model, not rules of thumb.
Why planning is the highest-leverage lever
Commerce sets the menu the planner has to work with. Sorting decides whether the planner's decisions are physically executable. Sawing, drying, and dry sorting execute or destroy the plan. Planning sits in the middle of the chain — and it has the largest single impact on lumber yield improvement because it's the only step that chooses. Every other step either prepares the input for that choice or carries out its consequences.
Most sawmills under-invest in planning for a specific reason: planning doesn't look like work. The salesperson is on the phone. The saw is loud and visible. The kiln has temperature readouts and clear cycle times. The technologist is at a desk with a spreadsheet, deciding which cant pattern goes on the line tomorrow. From the outside, that looks like paperwork. In reality, that 30 minutes of choice usually determines a larger share of the mill's monthly margin than any single shift on the line.
The thesis of this article is simple and provable in numbers: the way most mills currently plan — one shift at a time, with the order book that happens to be open, against rules of thumb the technologist has built up over years — leaves several percentage points of yield and several percentage points of order coverage on the table every week. The fix is structural, not personal. Replace the shift-at-a-time horizon with a planning window. Replace the closed order book with an order pool of roughly 1.5× the production volume. Replace rules of thumb with a yield model that quantifies the trade-offs. Each step compounds with the others.
The rest of this article walks through each piece of that fix.
The shift trap — why mills default to short-horizon planning
Before getting to what horizon planning is, it's worth understanding why most mills don't do it. The short answer: shift-by-shift planning feels safer. It maps to how the rest of the mill is organized. And it almost always loses.
Why shift planning is the default
Three forces push mills toward the short horizon:
The information the technologist has on Monday morning feels concrete. Today's sorted log buffer is what's in front of them. Today's open orders are in the order book. Today's saw line is set up the way it was on Friday. Tomorrow is more uncertain — a customer might push out, a saw might need an alignment pause, a log delivery might delay. So the technologist plans the day they can see and trusts they'll figure out the next day when it arrives.
Reactive culture rewards it. When a customer calls Friday afternoon with an urgent order, the response everyone respects is "we'll find a way to fit it in." That response is only possible if the plan is short. A two-week plan that says "we can fit you in week 3" feels less helpful than "we'll figure something out tomorrow," even when the two-week plan would deliver better economics for both sides.
Tooling enforces it. Spreadsheets are great for a single day's plan and miserable for a two-week rolling plan. ERPs record what happened but rarely propose what should happen next. Without dedicated planning software, the friction of planning a longer horizon is so high that no one does it.
The hidden costs of shift planning
The visible cost of shift planning is none — the mill keeps running. The hidden cost is yield and order coverage left on the floor every week, and it shows up in four ways:
- Forced fits. With one shift of orders and one shift of sorted logs, the planner has 1–2 cant pattern options. The chosen pattern is the least-bad option in a small set. Yield is whatever that least-bad option produces.
- Order whiplash. Orders that the planner couldn't fit today get dropped into tomorrow with no context. Tomorrow's planner — sometimes the same person, sometimes a different shift's technologist — starts from scratch.
- Kiln misalignment. The kiln pipeline is 5–10 days long. A planner working on today's saw line can't see the kiln charges that will need feeding next week. Half-empty charges become routine.
- Customer-service trade-offs. Without a horizon, every customer request is a binary: yes today, or no until further notice. Mills with a horizon can offer "we'll deliver in week 3" with confidence — and many customers prefer that to a rushed commitment.
The horizon principle — what changes when you plan on a window
Horizon planning is exactly what it sounds like: instead of optimizing one shift at a time, the planner optimizes a window — typically 1–2 weeks of production — as a single problem. Every shift in the window is still planned; the difference is that each shift's plan is chosen with knowledge of every other shift's plan, the full sorted log inventory across diameters, the full order pool, and the kiln pipeline downstream. This is the foundation of effective sawing batch planning: not "what runs next?" but "what runs across the window, and in what sequence?"
The Toyota parallel — why this idea is not new
The horizon principle is not a sawmill invention. The most studied version of it is the Toyota Production System, developed at Toyota Motor Corporation between the late 1940s and the 1970s and codified in the lean manufacturing literature since. The core problem TPS solved — how to run a complex, multi-variant production line at high throughput without drowning in either inventory or changeover time — is the same problem a sawmill faces, with different equipment.
Three TPS ideas map directly onto sawmill optimization:
- Heijunka — production leveling across a window. Toyota didn't schedule production one car at a time. They smoothed demand across a planning window and sequenced production to balance the line, treating each window as a single problem to optimize. In sawmill terms: that's the planning horizon. Same logic, different product.
- Just-in-Time, with the right buffer. Toyota minimized in-process inventory but did not eliminate it — they sized buffers to absorb variation without choking the line. Sawmills face the same trade-off: too small an order pool and the planner has no choice; too large and working capital balloons. The 1.5× order pool is the sawmill expression of the JIT principle applied to upstream order book, not downstream parts inventory.
- Andon — surface problems immediately. When something went wrong on a Toyota line, the operator pulled a cord; the whole line learned within seconds. Sawmills traditionally surface problems at month-end review. The same-day plan/actual reconciliation described later in this article is the andon principle adapted to lumber yield improvement: surface the variance the day it happens, when the cause is still investigable.
The analogy is not perfect — Toyota assembles standardized parts into standardized products, while a sawmill receives variable raw input (logs) and produces a product mix that has to match a variable demand book. Sawmills are closer to a job shop than to a final-assembly line. But the underlying principles — wider planning horizon, right-sized buffer, real-time feedback — transfer directly, and they are why the same structural change to planning produces the same kind of step-change in performance.
The next four sections walk through the four specific mechanisms that make horizon planning win in a sawmill. Each of them has a TPS analog, noted in passing.
Mechanism 1 — Pattern choice freedom
With one shift of inventory and one shift of orders, the planner has 1–3 viable cant patterns to choose from. With a 1–2 week horizon, the planner has 12–20 candidate patterns across all available diameters and all open orders, and can pick the 5–7 that will run during the window.
This isn't a marginal improvement; it's a categorical one. The economic optimum across 20 options is meaningfully better than the optimum across 3, and the gap grows the more constrained the small set is. A planner working from a small set gets the least-bad option. A planner working from a large set gets a genuinely good option.
Mechanism 2 — Inventory timing
Sorted log inventory shifts day by day. A diameter that's well-stocked Monday may run dry Wednesday and refill by Friday. A planner working on today's shift takes whatever is in front of them and cuts. A planner working on a horizon can wait — assign a particular set of orders to Thursday's shift because by then the 200 mm bin will be full, while running Monday's shift on 180 mm.
The savings here are not yield-per-cut savings; they are option-value savings. The horizon lets the planner spend log inventory at the moment when its match to orders is best, rather than at the moment it's available.
Mechanism 3 — Cross-shift continuity
Shift planning treats each shift as independent. An order that was 60% cleared at the end of Monday gets dropped into Tuesday with no continuity. Tuesday's planner — possibly the next-shift technologist — starts the cant pattern decision from scratch, often choosing differently. The remaining 40% of Monday's order takes more setups, more changeovers, more yield loss than it should.
Horizon planning carries continuity across shifts. The same cant pattern runs Monday afternoon into Tuesday morning to clear the open order; the changeover happens once, at the end of the order rather than at the end of each shift.
Mechanism 4 — Kiln pipeline alignment
The kiln pipeline operates on a 5–10 day cycle. Boards cut Monday don't dry until next week. A planner working on today's shift can't see which kiln charges will be ready for filling next Tuesday, so kiln charges get assembled reactively from whatever boards happen to come off the line.
Horizon planning aligns the cut to the dry. A planner who can see that next Tuesday's kiln charge needs 22 mm boards can plan this Monday's cant pattern to produce 22 mm boards in the right volume — turning the kiln from a passive consumer into an active partner in the plan. This is the single largest source of "free" capacity recovery in most mills, because most kilns run 15–20% under loaded on average.
The 1.5× order pool — sizing the input
Horizon planning only works if there's something to choose from. A 2-week horizon over an order book exactly equal to 2 weeks of production volume still produces forced fits — every order has to go somewhere, and the optimization problem is one with no slack. To unlock the value of the horizon, the order pool needs to be larger than the production volume of the window.
The practical sweet spot is roughly 1.5× the production volume of the planning window. Pool 1.5× means: for a 2-week window producing 4 000 m³, sales should have 6 000 m³ of open, committed orders in the pool, eligible to be cut from any of those 4 000 m³ of production.
Why 1.5× and not 1.0× or 2.0×
Below 1.3× — forced fits. Every order in the book has to go through the line in the planning window. The planner has no slack. The optimization problem degenerates back to the shift trap, just on a longer horizon.
1.3× to 1.5× — workable. The planner has some choice, particularly for high-volume specs that overlap multiple diameters. Yield improvements show up. Order coverage improves. This is the minimum viable pool.
1.5× — sweet spot. Pattern choice is genuinely open. The planner can balance yield, order coverage, and kiln pipeline simultaneously. Working capital tied up in promised-but-uncut orders is manageable. Customer service is preserved because most orders clear within their commit window.
1.7× to 2.0× — diminishing returns plus real costs. The marginal yield gain from a larger pool flattens out — there are only so many patterns that ever get chosen, no matter how many candidates exist. Meanwhile, working capital tied up in promised orders grows, customers wait longer than they should, and the long tail of small orders in the pool starts to look more like overbooking than optimization.
How to grow the pool from 1.0× to 1.5×
For most mills, the pool starts well below 1.0× — sales books orders just-in-time, and the order book at any moment barely covers the next week of production. Growing the pool to 1.5× takes deliberate commercial action. Three practices help:
- Forward-sell with delivery flexibility. Offer a 2–3 week delivery window instead of a fixed date. Many customers will accept the window in exchange for slightly better pricing or priority access.
- Recruit complementary orders, not duplicates. Sales should be looking for orders that complement what's already in the book — thin specs to match dominant thick orders, backfill orders for under-subscribed kiln charges (see Practice 1B in the Commerce article).
- Build relationships with anchor customers willing to commit on a window. A short list of customers who commit volume on a 2-week window — not a fixed date — gives the planner the slack they need. These customers usually get preferential pricing or service in exchange.
The yield model — yield × order coverage, not yield alone
A larger pool of orders gives the planner more options. The yield model is what tells the planner which option is best. And "best" turns out to be different from what most spreadsheet models implicitly assume.
The default planning instinct — and the default that most spreadsheet yield calculators encode — is to maximize yield percentage. Of the candidate cant patterns for a given log diameter, pick the one that produces the highest cubic meters of timber per cubic meter of log. This is the wrong objective, and the failure mode is specific.
The yield-only trap
A cant pattern with 53% theoretical yield that produces boards no current customer wants is worse than a pattern with 51% theoretical yield that clears two open orders.
The 53% pattern produces 2 pp more timber per log. The 51% pattern produces 2 pp less timber per log — but every board has a buyer at standard contract price. The 53% pattern's "extra" boards go to stock (working capital tied up) or to commodity sale (at meaningfully lower price per m³). On the income statement, the 51% pattern wins almost every time.
The right objective function is yield × order coverage — the cubic meters of timber that clear an open order at standard price per cubic meter of log. Equivalently: revenue per m³ of log, calculated at the price the order actually pays, not at the price the boards would fetch in the spot market.
This shifts every decision. Patterns are now ranked by their expected revenue contribution to the order book in the planning window — not by their abstract yield. Pattern selection respects what's open, not what's possible in theory. The technologist still chooses, but they choose with the right number in front of them.
Worked example — Monday morning at the mill
Put all three pieces — horizon, pool, yield model — together into a single Monday-morning sawing batch planning scenario. The numbers are illustrative, but the structure of the decision is exactly what a planner faces every week.
The setup
Production target this week: 2 000 m³ of log throughput, single-line softwood mill.
Sorted log inventory available:
| Diameter class | Volume in buffer | Replenishment expected |
|---|---|---|
| 160 mm | 600 m³ | steady |
| 180 mm | 800 m³ | peak, draws down by Thursday |
| 200 mm | 600 m³ | refill Wed–Fri |
Open orders in the book this Monday morning:
| Spec | Outstanding volume | Delivery commit |
|---|---|---|
| 22×100 | 800 m³ | this week (priority) |
| 25×100 | 400 m³ | this week |
| 32×100 | 300 m³ | this week |
| 47×100 | 500 m³ | this week |
| 50×150 | 200 m³ | this week |
Total open orders this week: 2 200 m³. Production target: 2 000 m³. Order pool / production = 1.1×. Too tight to optimize.
Pool 1.1× — what the planner has to do
With essentially no slack in the pool, every order has to be cut this week. The planner picks the cant pattern with the highest yield for each diameter class and lets the orders fall where they fall.
For 180 mm logs, the highest-yield pattern available is Pattern A — 52% net yield, clears 40% of the 22×100 order. The planner picks A.
Result at end of week: 22×100 only 40% cleared (320 m³ of the 800 m³ priority order shipped). The surplus boards from Pattern A — high-yield but not matched to the priority spec — accumulate as stock or sell at commodity price. The technologist hits the yield number but the commercial team is on the phone all week explaining the partial delivery on 22×100.
Pool 1.5× — what changes
Now imagine sales has spent the past two weeks recruiting complementary orders into the pool. The pool grows by 800 m³ of new orders — sized to complement the existing book, not duplicate it:
- 22×125 — 300 m³ (deliverable in 2 weeks)
- 22×75 — 200 m³ (backfill for kiln charge)
- 32×150 — 200 m³ (deliverable in 2 weeks)
- 50×100 — 100 m³ (deliverable in 2 weeks)
Total order pool: 3 000 m³. Production target: 2 000 m³. Order pool / production = 1.5×. The planner now has genuine choice.
For 180 mm logs, SawmillSmart proposes three candidate cant patterns:
| Pattern | Net yield | Orders it clears (% of pattern output to standing orders) |
|---|---|---|
| A | 52% | 22×100 (40% of output) — rest unsold |
| B | 51% | 22×100 (60% of output) — rest unsold |
| C | 49% | 22×100 (60% of output) + 22×125 (25%) + 22×75 (15%) — 100% to open orders |
With pool 1.1×, the planner picked A. Yield 52%, but coverage was a problem.
With pool 1.5×, the planner picks C. The pattern delivers 49% yield — 3 percentage points below A — but every board it produces clears a standing order at contract price. There's no commodity falldown. The 22×100 priority order moves from 40% cleared to 80% cleared on the same shift. The 22×125 and 22×75 backfill orders progress in parallel.
The economic math
Crucially, the planner has not lost yield value. They have traded headline yield for revenue per m³. Walk the math at €280/m³ as the standing-order contract price and €240/m³ as the commodity falldown price:
| Metric | Pattern A (yield-only) | Pattern C (yield × coverage) |
|---|---|---|
| Net yield | 52% | 49% |
| Timber from 1 000 m³ of logs | 520 m³ | 490 m³ |
| Volume to standing orders | 208 m³ (40%) | 490 m³ (100%) |
| Volume to commodity | 312 m³ (60%) | 0 m³ |
| Revenue at €280 / €240 per m³ | €133 120 | €137 200 |
| Effective price per m³ of timber | €256 | €280 |
| Revenue per m³ of log input | €133 | €137 |
Pattern C produces €4 more revenue per m³ of log input than Pattern A, while looking — to anyone who watches only the yield column — like the worse pattern. At 2 000 m³/week throughput, that's an extra €8 000/week, or roughly €400 000/year, on yield model selection alone. The mill is the same. The crew is the same. The logs are the same. Only the planning logic changed.
This is the case for the yield model in one example. The shift planner with pool 1.1× picks A. The horizon planner with pool 1.5× and a yield-times-coverage model picks C. Same data, different software, different answer.
The anonymized case — yield, coverage, and revenue together
The worked example above is illustrative. The case below is a real lumber yield improvement project, anonymized to preserve commercial confidentiality.
A medium-size European softwood sawmill — approximately 140 000 m³ of annual log intake, mixed pine and spruce, single-line production with a multi-rip primary breakdown and an in-line splitter — moved from spreadsheet-based shift planning to horizon planning with a 1.5× order pool and a yield-coverage model. The before/after data is from two consecutive quarters with comparable log mix and order book composition.
What the three metrics tell you together
Most planning conversations focus on yield alone because it's the easiest number to track. The case above shows why looking at yield alone misses the larger story.
Yield rose 5 percentage points. From 46% to 51%. This is the headline number — same logs producing more timber. Worth approximately €1.4M/year in additional timber output at average market price.
Order coverage rose 24 percentage points. From 64% to 88%. The mill went from selling roughly two-thirds of its timber against open contracts to selling almost all of it. The other 36% (before) was going to stock buildup or commodity sale at discount. This shift alone — the same volume of timber, but a higher share into standing orders — was worth approximately €400 K/year in revenue mix uplift.
Average revenue per m³ of timber rose €18. From €247 to €265. This is the consequence of the order coverage improvement: timber that lands on a contract is paid at contract price, while timber that doesn't lands in commodity at €30–50 less per m³. The shift in revenue per m³ compounds with the yield gain to produce the headline annual margin impact.
Together, these three metrics tell the full story that yield alone hides. The mill produced more timber, sold a larger share of it at standing-order prices, and earned more per m³ of what it sold. None of those three is the cause of the others — they are all downstream consequences of the same upstream change: better planning.
How SawmillSmart implements horizon planning
Everything above is the principle. SawmillSmart is the implementation, purpose-built for sawmill optimization in European softwood mills. This section walks through how the software does each of the three things that distinguish horizon planning from spreadsheet sawing batch planning — and one section on what it deliberately does not do.
SawmillSmart is sawmill production planning software purpose-built for European softwood mills. It sits in the planning layer of the mill — between the order book (commercial side) and the execution systems (saw line control, kiln scheduling). It is not an ERP and is not a substitute for one. It is the optimization engine that turns sales' order pool and operations' log inventory into a daily cut plan.
Practice 1 — Cant pattern generation against your real order book
The first thing SawmillSmart does differently from spreadsheets is generate cant patterns against the actual order pool, not against an abstract yield model.
How it works. The planner imports the current order pool — each open order with its spec, outstanding volume, delivery commit, and contract price — together with the sorted log inventory by diameter class. SawmillSmart runs the cant pattern generator for each diameter and produces a ranked list of feasible patterns. The ranking is by yield × order coverage at contract price — not by yield in isolation.
What's different. A spreadsheet yield calculator can tell you that a 72×144 pattern on 180 mm logs produces 53% yield. It can't tell you whether the boards that pattern produces actually match an open order at standing-order price. SawmillSmart joins those two questions into one decision: for this set of logs and this set of orders, which patterns are worth running?
Output the planner sees. A ranked table of cant patterns for each diameter class in the buffer, with predicted yield, expected order coverage by spec, and projected revenue per m³ of log input. The technologist still chooses — but they choose with quantified consequences for each option.
Practice 2 — Yield forecast before the cut
The second thing SawmillSmart does is predict the yield of each candidate cant pattern before the cant goes on the line. Not a generic "this pattern should produce around 50%" — a specific forecast for the actual log batch in the buffer, with volume and grade mix broken down by output board.
Why this matters. Without a forecast, every cant pattern is a bet. The technologist commits the pattern, the line runs it, and the actual yield shows up at end of shift. By then it's too late to choose differently. With a forecast, the planner sees the expected output before committing — and can compare patterns on apples-to-apples basis.
How the forecast is built. SawmillSmart uses a calibrated yield model based on the mill's specific equipment, log mix, and historical performance. The model is mill-specific — calibration during deployment uses several weeks of plan/actual data from the mill to produce forecasts that match what the line actually delivers, not what a generic textbook predicts.
What the planner decides on. "This pattern delivers 51% net yield and clears 60% of the open 22×100 order. The next-best pattern delivers 49% net yield and clears 80% of the order plus 40% of the 22×125 backfill. Which do we run?" — instead of "this pattern looks reasonable, let's see what comes out."
Practice 3 — Plan/actual reconciliation by shift
The third thing SawmillSmart does is reconcile planned versus actual results at the end of every shift — not at month-end, not at quarter-end, but the same day the shift runs.
What gets reconciled. For each cant pattern that ran during the shift, SawmillSmart compares planned vs. actual: yield by output board, grade mix, order coverage, and revenue contribution. Drift between plan and actual is visible at end of shift, broken down by pattern, by diameter, by order.
Why same-day reconciliation matters. When a pattern misses its forecast by 2 percentage points and the technologist sees it the next morning, the cause is still fresh: a particular log batch was wetter than expected, a saw alignment drifted, an upstream sorter had a calibration issue, an operator made a setup change. These causes are recoverable when the gap is visible immediately. By month-end review, every cause has been overwritten by ten others and the gap is unattributable.
The compounding effect. This is what makes a 5-percentage-point yield gain repeatable rather than a one-off. Plan/actual reconciliation creates a feedback loop: each shift's variance feeds the calibration of the next shift's forecast, and over time the forecasts get more accurate, the technologist trusts them more, and the choice quality improves.
What SawmillSmart deliberately does not do
Setting scope honestly matters. SawmillSmart is purpose-built for the planning step. It is not an ERP, not a CRM, not a kiln controller, not a sorter calibrator. Specifically:
- Not an ERP. SawmillSmart does not replace inventory accounting, financial postings, or invoice management. The mill's ERP remains the system of record; SawmillSmart consumes the order pool and log inventory data and returns the cant plan.
- Not a kiln controller. SawmillSmart aligns the cant plan with the kiln pipeline — it tells the planner what to cut for next Tuesday's kiln charge — but the kiln cycle itself is run by the kiln control system.
- Not a sorter calibrator. SawmillSmart assumes the log sorter delivers what it says it delivers. Mis-sort calibration is upstream — see Part 3 (Log Sorting).
- Not order entry. Sales books orders in the CRM or order management system the mill already uses. SawmillSmart consumes the order pool from there.
The scope is deliberate: SawmillSmart does one thing — turn the order pool and the log inventory into the best cant plan — and integrates cleanly with the systems that do everything else. That focus is what makes it work.
What planning is NOT — three anti-patterns
Conversations about sawmill planning often confuse three distinct functions. Each of them lives next to planning in the workflow, and each is sometimes called planning by people who use the word loosely. None of them is what this article is about, and conflating them is one reason mills under-invest in actual planning.
Anti-pattern 1 — Planning is not scheduling
Scheduling answers the question "what runs when?" — Monday morning shift, Tuesday afternoon shift, Friday night. Scheduling is a calendar exercise. It cares about resource availability, shift coverage, and maintenance windows.
Planning answers a different question: "what should we cut, from which logs, against which orders?" — a yield-and-coverage exercise. Planning's output feeds the schedule, but the schedule doesn't optimize anything by itself. A perfectly scheduled mill running sub-optimal cant patterns produces sub-optimal yield with great timing.
Most ERPs do scheduling. Few do planning. Buying a better scheduler doesn't fix the planning gap.
Anti-pattern 2 — Planning is not queueing
Queueing is the order in which open orders get cut. First-in-first-out. Largest-first. By delivery date. By customer priority. These are queueing rules, and most mills have them — explicit or implicit.
Queueing rules are downstream of planning. The planner decides which cant patterns to run; queueing only matters once that decision is made, to assign output to specific orders. A great queueing system applied to a bad cant pattern decision produces orderly delivery of yield-poor cuts.
Anti-pattern 3 — Planning is not order entry
Order entry is the workflow of getting a customer order into the system: spec, volume, delivery, price. It's a commercial function (see sawmill commerce).
The order book is the input to planning, not planning itself. A clean order book is a precondition for good planning; it doesn't replace it. Mills that have invested heavily in order entry tooling sometimes wonder why their yield hasn't improved — it's because they've made the input cleaner without changing the optimization step that consumes it.
What to do this week — checklist
Five diagnostic actions that surface where the planning gap is at your mill and what to do about it.
Sawmill planning — week 1 checklist
- Calculate your order pool ratio. Total volume of open orders ÷ production volume in the planning window. Below 1.3× = no horizon planning possible regardless of tooling. Above 1.7× = pool is too big; customer service is at risk.
- Audit one week of plan vs. actual yield. Pull the planned yield for each shift in the past week and compare to actual. Gap larger than 2 percentage points = the plan isn't anchored in a real yield model.
- Pick one open order and trace it through last week. For your single highest-priority open order, count how many separate cant patterns it appeared in across the week. More than 3 = continuity is broken; the order is being re-planned every shift.
- Walk to the kiln. Count under-loaded charges in the last week. Each under-loaded charge is downstream evidence of planning that didn't see the kiln pipeline.
- Ask the technologist what tooling they actually use to plan. If the answer is "Excel" or "we always run pattern X for diameter Y" — there is no yield model in the loop. That's the planning gap.
Where to go next
If two or more of the checklist items above came back red, the planning gap is large enough to be worth quantifying directly. SawmillSmart runs a yield potential analysis on your real data — last quarter's order book, last quarter's log mix — and produces a quantified estimate of the yield, coverage, and revenue uplift available from horizon planning at your mill.
Sources & further reading
The principles and ranges referenced in this article (1.5× order pool sweet spot, yield × order coverage objective, plan/actual reconciliation) are grounded in operations research literature on lumber recovery optimization and the author's project work with European softwood sawmills. The anonymized case is based on a real deployment.
Operations research and lumber recovery
- FPInnovations — Lumber recovery research and sawmill optimization. Peer-reviewed research on cant pattern optimization, log breakdown strategies, and yield modeling for softwood mills. fpinnovations.ca
- USDA Forest Service — Forest Products Laboratory technical reports. Reference research on lumber recovery economics, breakdown patterns, and value optimization. fpl.fs.usda.gov
- Skogforsk — Research on log bucking, sorting, and breakdown for Nordic sawmilling. Practical research linking log selection to downstream value. skogforsk.se
- Wood Resources International — Wood Markets reports. Independent global wood-products market analysis, including price spreads between contract and commodity timber. woodprices.com
Industry data and market context
- Eurostat — Sawnwood production statistics (PRODCOM). Quarterly production volumes and turnover for EU sawmills. ec.europa.eu/eurostat
- FAO — ForeSTAT Forestry Production and Trade. Global softwood and hardwood production, trade, and consumption time series. fao.org/forestry/statistics
- EOS — European Organisation of the Sawmill Industry, Annual Report. Production, capacity utilization, and outlook for European softwood sawmills. eos-oes.eu
Standards relevant to planning outputs
- EN 1313-1 — Round and sawn timber: Permitted deviations and preferred sizes (Part 1: Softwood sawn timber). Defines tolerance classes that drive consolidated spec planning. cen.eu
- EN 14298 — Sawn timber: Assessment of drying quality. Reference standard for the kiln output the planning step targets. cen.eu
- EN 14081-1 — Strength-graded structural timber. Defines grade specifications relevant to grade-mix forecasting. cen.eu
The anonymized case (yield 46% → 51%, order coverage 64% → 88%, revenue +€18/m³) is based on a real European softwood sawmill deployment. Client identification has been omitted to preserve commercial confidentiality. The Monday-morning worked example uses representative numbers for illustration and is not drawn from a specific mill.